Resilience as health promotion in action: University students who grew up amid violence directed towards their mothers
Bibliographic record
Abstract
Abstract\nViolence against women is among the greatest threats to the health of our population. An estimated three hundred and sixty thousand children in Canada, and over two million worldwide are exposed to violence in their homes (UNICEF, 2006). Growing up amidst such violence seriously compromises children’s capacities for healthy development. Violence against women is not limited by culture, geography or socioeconomic status. It constitutes one of the most pervasive and yet least openly discussed human rights violations and public health issues known today.\nResearchers and allied health professionals generally agree that children whose development has been interfered with by exposure to violence against women experience more adjustment problems than non-exposed counterparts. Cognitive, emotional and behavioural mechanisms employed in children’s adapting to such experiences are as varied as the children themselves. Children have incredible capacities for resilience but it is a social process that requires efficacy of person and of place. There is a need to understand the dynamic process of navigating a pathway to health promoting resources during and in the aftermath of exposure to violence against women during childhood.\nThis study utilized Charmaz’s (2000) constructivist grounded theory to co-construct with participants a theory of resilience to childhood exposure to violence against women and subsequent transition to university. Based upon their own experiences of self-identified resilience to growing up amid such violence, the outcome of this research was that the basic social process of resilience to the aforementioned is resolving the dialectical tensions of tolerance and transformation. This process unifies the three core categories of assessing needs and accessing resources, experiencing solidarity despite isolation and oppression, and acceptingthe present while dreaming of the future. At any given moment during the process of resilience participants oscillated between willingness to accept their experiences and willfulness to change them. The health promotion framework influenced generation of possible applications of findings including combating censorship, creating policy that protects and serves the needs of children, and enhanced social services that address the impact of growing up amidst violence against women on children.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".